A project of the code repository mining seminar of the software architecture group at the Hasso Plattner Institute at the University of Potsdam
This is the repository for the "Language Influence on Code Style" project of the code repository mining seminar SS20 of the software architecture group at the Hasso Plattner Institute at the University of Potsdam.
The project aims at exploring the influence of learning a new programming language on the code style of the old languages. For this, Java code quality of Java developers that started learning Javascript or Python is compared to the one of Java developers that didn't. Code quality in this context purely refers to the code constitution, not its goodness.
Most of the work that has been done in this project can be found in Jupyter notebooks. These are split horizontally by the problems they address.
A good starting point is the DataExplorer notebook. It shows how the project base data is extracted from the GHTorrent dataset. It consists mainly of sql queries for view and table creation and for exploratory questions. The notebook results in views and tables, notably lb_polyglots and lb_controlgroup which include the groups that are compared in this project. The full data schema for this notebook can be found in this diagram.
The data from the data explorer is taken for the RepoAnalysis notebook. This notebook describes the three big analysis runs that lead to the project commit metric data and the improvements that have been done between these runs. It brings together results from several sub projects: The two experiment groups from the data explorer are used to determine the sets of repositories that should be analyzed. Downloading and curating the repositories is done by the repoLibrarian module. Information on the usage and development of this module can be found in the RepoLibrarian notebook. The repositories are then analyzed with the help of the repoAnalysis module. The development history of this module can be found in the RepoAnalysis_Historical notebook. Additionally, insights during the analysis runs have been incorporated into the module and documented in the RepoAnalysis notebook.
The resulting analysis data for each run is combined with the user data from the data explorer in the Results_Iteration notebooks #1, #2, and #3 respectively. These notebooks describe the evaluations of the analysis runs, with the first resulting in many insights on the methodology and the second showing the feasability of the realization of these insights. The third run then uses the now proven technology, scales up the input data, and evaluates a bit more in depth.
The UserAnalysis notebook is a small sideproject analyzing the intermediate experiment groups (especially the polyglots) from the data explorer.
The dbUtils notebook provides utility functions for the connection to the database and for long running queries of all kind.
docs/ and results/ provide additional material like logs and exported diagrams.
Besides the Notebooks, further artifacts can be explored:
Aggregation of user data in the DataExplorer mainly lead to the creation of the lb_polyglots and lb_controlgroup tables, which contain information on java developers that have been classified as (not) learning Python or Javascript as secondary language.
Note: All resulting tables are in schema named crm20.
Analysis of selected repositories in the RepoAnalysis lead to three result sets that can be found in the tables lb_results1, lb_results2, and lb_results3, the evaluation of which can be found in the Results_Iteration notebooks #1, #2, and #3.
The project has shown the technical possibility to analyze developer influence on project code metrics fast. It includes useful technical artifacts to ease further projects in code repository mining.
The project also yielded commit metric data sets for programmers categorized as Java only and polyglot (multilingual) programmers that can be used for further scientific analysis. It also yielded the methods to reproduce and extend this dataset.
Evaluation of the collected data has shown that for Java programmers there is a high probability for for a correlation between learning secondary languages Python or Javascript and having different constitutional code quality. The concrete nature of the correlation is uncertain for some metrics and might motivate further evaluations.
There are multiple points for future projects: Calculated code metric functions can be refined and extended by new metrics. The created data can be further analyzed, especially taking temporal aspects into account. It can also be scaled up to get scientifically proofer results.
Furthermore, the resulting technology from this project can be used to analyze different sets of developers, e.g. with different language combinations or with a completely different research question or to fuel completely different code repository mining projects.
The required python modules can be installed in a Python 3 environment with:
pip install -r requirements.txt
Jupyter lab must be installed to explore the notebooks of this project. Additionally, the Jupyter table of contents extension is recommended because it numbers all headings and allows quick navigation through the notebooks:
jupyter labextension install @jupyterlab/toc
The data is based on a fork of the GHTorrent database. The engine variable of the dbUtils module must be adapted to a valid connection.